From 3cb530cf97ea3ad814dc3eff92f608ffdceef01b Mon Sep 17 00:00:00 2001
From: Dipesh Babu <dipeshmahato@outlook.com>
Date: Mon, 9 Feb 2026 20:46:31 -0500
Subject: [PATCH] fix RoPE cache overflow with kv-cache by growing rope buffers

---
 nanochat/gpt.py | 43 +++++++++++++++++++++++++++++++++++++++----
 1 file changed, 39 insertions(+), 4 deletions(-)

diff --git a/nanochat/gpt.py b/nanochat/gpt.py
index 208acd1..cb1fe7f 100644
--- a/nanochat/gpt.py
+++ b/nanochat/gpt.py
@@ -22,6 +22,8 @@ import torch.nn.functional as F
 from nanochat.common import get_dist_info, print0
 from nanochat.optim import MuonAdamW, DistMuonAdamW
 
+from typing import Optional
+
 # Our custom Flash Attention module that automatically uses FA3 on Hopper+ and SDPA fallback elsewhere
 from nanochat.flash_attention import flash_attn
 
@@ -185,6 +187,37 @@ class GPT(nn.Module):
         self.register_buffer("cos", cos, persistent=False) # persistent=False means it's not saved to the checkpoint
         self.register_buffer("sin", sin, persistent=False)
 
+    def _ensure_rope_cache(self, needed_seq_len: int):
+        """
+        Ensure rotary embedding cache (cos/sin) is long enough.
+        We grow the cache lazily to avoid evaluation crashes on long prompts.
+        """
+        # existing cache length
+        cur = self.cos.size(1)
+        if needed_seq_len <= cur:
+            return
+
+        # grow to next power-of-two for amortized behavior
+        new_len = 1
+        while new_len < needed_seq_len:
+            new_len *= 2
+
+        head_dim = self.config.n_embd // self.config.n_head
+        device = self.cos.device
+
+        cos, sin = self._precompute_rotary_embeddings(
+            seq_len=new_len,
+            head_dim=head_dim,
+            device=device,
+        )
+        # keep dtype consistent with existing buffers
+        cos = cos.to(dtype=self.cos.dtype)
+        sin = sin.to(dtype=self.sin.dtype)
+
+        # re-register buffers (safe overwrite)
+        self.register_buffer("cos", cos, persistent=False)
+        self.register_buffer("sin", sin, persistent=False)
+
     @torch.no_grad()
     def init_weights(self):
         """
@@ -388,12 +421,14 @@ class GPT(nn.Module):
     def forward(self, idx, targets=None, kv_cache=None, loss_reduction='mean'):
         B, T = idx.size()
 
-        # Grab the rotary embeddings for the current sequence length (they are of shape (1, seq_len, 1, head_dim/2))
-        assert T <= self.cos.size(1), f"Sequence length grew beyond the rotary embeddings cache: {T} > {self.cos.size(1)}"
+        T0 = 0 if kv_cache is None else kv_cache.get_pos()
+
+        # Ensure cache covers absolute positions [T0, T0+T)
+        self._ensure_rope_cache(T0 + T)
+
         assert idx.device == self.cos.device, f"Rotary embeddings and idx are on different devices: {idx.device} != {self.cos.device}"
         assert self.cos.dtype == torch.bfloat16, "Rotary embeddings must be in bfloat16"
-        # if kv cache exists, we need to offset the rotary embeddings to the current position in the cache
-        T0 = 0 if kv_cache is None else kv_cache.get_pos()
+        
         cos_sin = self.cos[:, T0:T0+T], self.sin[:, T0:T0+T] # truncate cache to current sequence length
 
         # Forward the trunk of the Transformer